Project Summary Tractions exerted by individual cells on their surroundings play a critical role in mechanical events in biology such as tissue contraction, folding, cell shape changes, or cell movements, and in many basic cellular functions such as biochemical signaling, proliferation, and differentiation. These processes are in turn implicated in the progression of diseases like cancer, atherosclerosis, and other chronic fibrotic conditions. Recently, this remarkable link has been utilized to develop exciting new therapeutic interventions that rely on disrupting mechano-signaling machinery within the cell, and the pathways that lead to the remodeling of the extra-cellular matrix (ECM). Techniques that can precisely quantify the spatial variation and heterogeneity of cellular traction within and between cells have found important applications in understanding and controlling these processes. Of these, three-dimensional traction force microscopy (3D TFM) has emerged as a particularly valuable tool since it is applied to cells embedded in a three-dimensional ECM, the natural state for most cells. Current 3D TFM approaches are challenged by the critical steps of using optical images to generate a 3D geometrical model of the matrix surrounding the cell, and inferring cellular tractions from displacement estimates of micro-beads embedded in the matrix. Approximations incurred in these steps lead to significant errors in computed tractions that in turn lead to erroneous biological conclusions. Thus there is critical need to develop more accurate and high resolution 3D TFM techniques. The long-term objective of the proposed research is to improve and automate the 3D TFM process so that it can be effectively used to answer mechanobiological questions and design new therapeutic interventions. This will be accomplished by (a) applying advanced segmentation and mesh generation techniques to optical images to generate 3D geometric models and finite element meshes of the matrix surrounding a cell, and (b) by developing and implementing new algorithms to determine the spatial distribution of cellular tractions from measured micro-beads displacements, while accounting the nonlinear elastic response of the matrix. These developments will be validated through benchmark studies, and their utility will be demonstrated by quantifying the traction exerted by cancer cells embedded in a synthetic extracellular matrix.